An Interpretable Deep Learning Approach for Morphological Script Type Analysis

TL;DR

This paper introduces an interpretable deep learning method for classifying medieval script types, enabling systematic analysis of handwriting morphology and bridging qualitative and quantitative approaches.

Contribution

It adapts a deep instance segmentation model to learn and compare character prototypes, improving objectivity and interpretability in palaeographical script classification.

Findings

Successfully applied to Textualis Formata script type

Provides tools for qualitative and quantitative script analysis

Bridges gap between qualitative observations and quantitative measurements

Abstract

Defining script types and establishing classification criteria for medieval handwriting is a central aspect of palaeographical analysis. However, existing typologies often encounter methodological challenges, such as descriptive limitations and subjective criteria. We propose an interpretable deep learning-based approach to morphological script type analysis, which enables systematic and objective analysis and contributes to bridging the gap between qualitative observations and quantitative measurements. More precisely, we adapt a deep instance segmentation method to learn comparable character prototypes, representative of letter morphology, and provide qualitative and quantitative tools for their comparison and analysis. We demonstrate our approach by applying it to the Textualis Formata script type and its two subtypes formalized by A. Derolez: Northern and Southern Textualis